1. Field of the Invention
The present invention relates to wireless communication systems, and in particular, to using adaptive antenna arrays in PCS and cellular CDMA networks for capacity enhancement.
2. Description of Related Art
A standard technique used by commercial wireless phone systems for increasing capacity is to divide the service region into spatial cells. Instead of using just one base station to serve all users in the region, a collection of base stations is used to independently service separate spatial cells. In such a cellular system, multiple users can reuse the same frequency channel without interfering with each other, provided the users access the system from different spatial cells. The cellular concept, therefore, is a simple type of spatial division multiple access (SDMA). Note that throughout this description, various acronyms will be used, which are listed and defined in the Table below.
In the case of digital communication, additional techniques can be used to increase capacity. One well-known method is to use spatial signal processing in code division multiple access (CDMA) systems. CDMA is normally a spread-spectrum technique that does not limit individual signals to narrow frequency channels but spreads the signals throughout the frequency spectrum of the entire band. Signals sharing the band are distinguished by assigning different orthogonal digital code sequences or spreading signals to each signal. Practical techniques for capacity enhancement in CDMA systems using an antenna array are described in commonly-owned U.S. patent application Ser.No. 08/929,638 entitled xe2x80x9cPRACTICAL SPACE-TIME RADIO METHOD FOR CDMA COMMUNICATION CAPACITY ENHANCEMENTxe2x80x9d and U.S. Prov. patent application Ser. No. 60/071,473, entitled xe2x80x9cFADING MITIGATION USING WIDE APERTURE ANTENNA ARRAY IN SPREAD SPECTRUM COMMUNICATION SYSTEMSxe2x80x9d and 60/077,979, entitled xe2x80x9cCAPACITY ENHANCEMENT FOR W-CDMA SYSTEMSxe2x80x9d, all of which are incorporated by reference in their entirety.
Spatial signal processing can be used in both the forward link (base station to mobile station) and reverse link (mobile station to base station) of a CDMA communication system to provide significant signal-to-noise ratio and capacity improvements. In the reverse link, spatial signal processing includes estimating a spatial signature (defined herein as the vector of antenna output signals, including multipath components, at a given time due to a transmitted signal at a certain location, such as described in xe2x80x9cExperimental Studies of Spatial Signature Variation at 900 MHz for Smart Antenna Systemsxe2x80x9d by S. S. Jeng, G. Xu, H. P. Lin, and W. J. Vogel, IEEE Trans. on Antennas and Propagation, vol. 46, no. 7, July 1998, pp. 953-962, which is incorporated by reference in its entirety) of an IS-95 based CDMA signal to determine multipath angle of arrival (AOA) values and coefficients. An IS-95 system is described in TIA/EIA/IS-95-A, xe2x80x9cMobile Station-Base Station Compatibility Standard for Dual-Mode Wideband Spread Spectrum Cellular Systemsxe2x80x9d, May 1995, which is incorporated by reference in its entirety. The reverse link coefficients are then used to combine a plurality of antenna output signals (after down-conversion to base band), i.e. beamforming. Thus, the ability to accurately estimate the spatial signature is an important objective in CDMA systems. However, with a frequency division duplexing (FDD) system, the forward link and the reverse link occupy different carrier frequencies or bands, but overlap in time. This difference between the forward and reverse link frequencies reduces the correlation between fading of the two links so that the two links have significantly different spatial signatures. Therefore, forward link beamforming using the reverse link spatial signature estimate is not possible. However, average AOA is generally preserved in FDD systems between the forward and reverse links for mobile stations far away from the base station.
Factors which limit the ability to provide for accurate spatial signature estimation include the fading rate (Doppler spread), angle spread, and delay spread profiles of the incoming signals. In particular, fast fading, which is created by the combination of multipath components of a signal being reflected from various elements (xe2x80x9cscatterersxe2x80x9d) in the neighborhood (xe2x80x9cscattering zonexe2x80x9d) of a moving transmitter with random phases, is a major concern in accurate spatial signature estimation. The wireless communication channel is assumed to have multiple scattering zones characterizing the signal propagation between the base station (BS) and the mobile station (MS). xe2x80x9cMobile Cellular Telecommunicationsxe2x80x9d by W. C. Lee, McGraw-Hill, New York, 1995, which is incorporated by reference in its entirety, describes scattering zones around the mobile station. The main scattering zone is in the neighborhood of the MS. Large objects, such as buildings and waterways, create other scattering zones. Metal objects in the vicinity of the BS can cause the signal to be reflected and can influence the transmission path. However, in most cases, the BS antenna array is located above the nearby scatterers, which are assumed to be less significant. As the fading rate or Doppler spread increases, the time available to collect coherent data (integration time) decreases. This problem becomes more severe as cellular systems move from the 800 MHz range to the 1900 MHz range or higher, which can increase the fading or Doppler spread by a factor of two or more. For example, a vehicle moving at 60 mph can induce a Doppler spread in excess of 180 Hz in a 1900 MHz system. In general, spatial signature estimation must take place in a duration that is an order of magnitude shorter than the period of the fading rate. For example, if the fading rate is 100 Hz, then spatial signature estimation must take place within 1 msec.
Various methods for spatial signature estimation or beamformer generation have been proposed. These methods can be characterized by the level of knowledge of the structure of the signal impinging on the antenna array and whether or not a training sequence is present. Characteristics of the temporal, spatial, spectral, or modulation structure of the impinging signal may be known and can be exploited in the spatial signature estimation, such as described in xe2x80x9cAlgebraic Methods for Deterministic Blind Beamformingxe2x80x9d by A. J. Van der Veen, Proceedings of IEEE, vol. 86, no. 10, October, 1998, pp. 1987-2008, which is incorporated by reference in its entirety. Many adaptive algorithms based on minimum mean-squared error (MMSE) or constant modulus (CM) and exploiting temporal or modulation signal structure to perform estimation of the spatial signature are well known, such as described in xe2x80x9cAlgebraic Methods for Deterministic Blind Beamformingxe2x80x9d by A. J. Van der Veen, referenced above, and in xe2x80x9cSpace-Time Processing for Wireless Communicationsxe2x80x9d by A. J. Paulraj and C. B. Papadias, IEEE Signal Processing Magazine, vol. 14, no. 6, November 1997, pp. 49-83 and xe2x80x9cAdaptive Filter Theoryxe2x80x9d by S. Haykin, Prentice-Hall, Englewood Cliffs, N.J., 1986, both of which are incorporated by reference in their entirety.
A disadvantage of these adaptive algorithms is that they generally do not exploit knowledge of the array spatial information or array manifold and generally require substantial time to converge.
The array manifold is a collection of array response vectors (where each array response vector is the set of antenna output signals at a given point in time due to a far-field signal with no multipath) created by stepping the angle of a narrow-band point source (in two or three-dimensional space, under far-field and no multipath conditions). The array manifold is a trace in M-dimensional space, where M is the number of antenna elements in the array. Array manifolds are well known and are described in references such as in xe2x80x9cAlgebraic Methods for Deterministic Blind Beamformingxe2x80x9d by A. J. Van der Veen, referenced above.
A signal arriving at the antenna array in a non-multipath situation produces a received vector that is on the array manifold. However, when multipath exists, the received vector is a linear combination of all arriving multipath wave fronts, which is no longer on the array manifold. The Euclidean distance between the array manifold and the received vector is a function of multipath level, multipath angle spread, and interference power and increases with an increase in each of these variables. Interference includes the sum of thermal noise and other incoming transmissions.
Another disadvantage of some MMSE adaptive algorithms based on nullifying specific interferers is that performance is reduced when the number of interference sources is large (typical for CDMA). Furthermore, using a dedicated pilot signal (e.g., for training) on the reverse link requires the pilot signal to be low power in order to minimize capacity loss in the reverse link. However, a lower power pilot in coherent demodulation requires a longer integration time to assure sufficient reference signal quality.
Also, an unknown or varying signal time of arrival (TOA) requires continuous xe2x80x9ctime searchingxe2x80x9d and determination of beamformer coefficients at each time hypothesis. In CDMA type systems, the signal timing must be recovered before any demodulation can take place. Hence, a search process is conducted by a series of hypotheses through which the system is varying the time of the reference correlating sequence and then cross-correlating with the incoming signal (e.g., IS-95). The time required by each hypothesis must be short to allow a quick search (assuming that recovery of signal timing cannot be done before determination of beamformer coefficients since there might not be sufficient signal-to-noise ratio at that point). Thus, any adaptive algorithm to determine the beamformer coefficients must provide fast convergence in order to achieve a reasonable overall search time.
Furthermore, MMSE algorithms operating in xe2x80x9cdecision-directedxe2x80x9d mode, as discussed in xe2x80x9cAdaptive Filter Theoryxe2x80x9d by S. Haykin, referenced above, tend to fail at low signal-to-interference ratio (SIR) conditions at the system input. In the presence of fast fading, instances of low SIR are common, which can cause extreme interference or even a xe2x80x9cdroppedxe2x80x9d call.
Therefore, a system and method are desired that provide channel estimation in both forward and reverse links and in fast fading and low SIR environments.
In accordance with the invention, known antenna array manifold information is exploited to provide for fast and accurate channel estimation and demodulation in both the forward and reverse links, thereby increasing capacity in PCS and cellular CDMA networks that use adaptive antenna arrays. On the reverse link, an xe2x80x9cextendedxe2x80x9d array manifold is used to assist the demodulator in maintaining a dynamic estimate of the spatial signature to use for beamforming and coherent demodulation. On the forward link, channel estimation is performed in the handset to provide a robust solution for beamforming.
A de-spread received signal vector yi (i=1 to N) from M antenna element output terminals corresponding to 1 of N possible received symbols is spatially correlated with the array manifold matrix C to produce a correlation vector p. The array manifold matrix C is an Mxc3x97K matrix, where M is the number of antenna elements, and K is the number of angles used to create the array manifold (e.g., K=256), i.e., each row of matrix C represents one antenna element of the array manifold, and each column of matrix C represents one angle in the array manifold. This matrix generally spans the whole M-dimensional space produced by the M-element antenna array.
The resulting correlation vector p represents a correlation between the de-spread, received vector corresponding to one of the N possible symbols with each of the K angles of the array. The spatial correlation is performed for each of the N possible received symbols to produce a Kxc3x97N matrix P, where the ith column of the p matrix contains values with magnitudes corresponding to the level of correlation between yi and the array manifold for all K angles. Finding the maximum element of the ith column of P is equivalent to selecting the column of the array manifold that is closest in Euclidean distance to the vector yi.
The P matrix is then used to produce a magnitude and angle of arrival (AOA) estimate each symbol time, which are accumulated to generate an AOA histogram. After low-pass filtering (or xe2x80x9csmoothingxe2x80x9d), this histogram will exhibit xe2x80x9cpeaksxe2x80x9d in the direction of the main scatterers and a distribution that follows the angular spread of the transmission source. The multipath scattering area size (or angle spread) can be estimated from the variance of the distribution around a peak. Thus, the histogram can be used to determine the AOA and angular distribution of the most probable signal paths.
Next, if the incoming signal angular distribution is known, a subspace V spanned by the columns of the array manifold matrix C associated with the given angular distribution (mean and variance of the signal AOA) can be defined. These columns (vectors) span a subspace that can be approximated by finding a set of L orthogonal vectors that contains most of the signal energy. Finding this reduced rank subspace {circumflex over (V)} can be done by singular value decomposition (or a similar procedure) of the original subspace and selecting the singular vectors corresponding to the larger singular values.
Since the de-spread, received antenna vector yi contains contributions from all directions, it contains energy in the full space spanned by the columns of the antenna array manifold C. Most interference can be filtered out of the received vector by projecting this vector yi into the confined subspace {circumflex over (V)}. Thus, the sampled received antenna vector (after de-spreading) is projected into this approximated subspace, {circumflex over (V)}, by a simple dot product of the received vector with each of the columns of matrix {circumflex over (V)}. The projection coefficient vectors corresponding to preliminary data symbol decisions are averaged over a predetermined number of symbols. The number of vectors to average is determined by the Doppler spread and is selected to correspond to a time period during which the spatial signature is relatively stable. Generally, this period is an order of magnitude shorter than the period of the Doppler spread. The averaged projection coefficient vector, Zavg, is then multiplied by the approximated subspace vectors from matrix {circumflex over (V)} to form the beamformer coefficient vector w. If more than one distinguishable AOA exists, several beamformers are used to track the multiple scattering zones.
The processed vector w contains less interference and noise than the original vector y because the subspace projection rejects all the components that are orthogonal to the selected subspace. Projecting the received vectors into a subspace of dimension L reduces the noise power by a factor of M/L, where M is the number of antenna elements. If the subspace is selected correctly, then the signal power is only slightly reduced. Thus, signal-to-noise ratio (SNR) for the beamformer coefficient vector is improved by approximately M/L. This reduces the amount of data that needs to be integrated for the purpose of estimating the beamformer coefficients. As a result, performance is improved (e.g., lower symbol error rate for a given SNR) in fast fading environments when compared with direct averaging of de-spread received vectors and compared to conventional two antenna systems with diversity combining.
The extended manifold concept also can be used to provide an initial beamformer coefficient vector for other methods such as MMSE adaptive algorithms. Since the initial coefficient vector is closer to the desired solution and has better SNR, an adaptive algorithm will converge more quickly.
Even partial knowledge of the array manifold can significantly reduce required integration times, provide improved stability, and simplify the computational process. The array manifold allows accurate AOA and multipath angle spread estimation using xe2x80x9csmoothedxe2x80x9d data, as described in U.S. Pat. App. xe2x80x9cPRACTICAL SPACE-TIME RADIO METHOD FOR CDMA COMMUNICATION CAPACITY ENHANCEMENTxe2x80x9d and U.S. Prov. Pat. Apps. xe2x80x9cFADING MITIGATION USING WIDE APERTURE ANTENNA ARRAY IN SPREAD SPECTRUM COMMUNICATION SYSTEMSxe2x80x9d and xe2x80x9cCAPACITY ENHANCEMENT FOR W-CDMA SYSTEMSxe2x80x9d, referenced above. The AOA and angle spread information can provide xe2x80x9cboundsxe2x80x9d for spatial signature estimation, thereby eliminating large beamformer errors even when the SIR becomes low.
Overall, the present invention offers 1) increased implementation efficiency since the system can be implemented cost-effectively within an ASIC not only for base stations, but also for handsets, 2) high speed operation suitable for both stationary and mobile applications, and 3) stable performance, i.e., not susceptible to convergence and stability issues associated with many adaptive spatial filtering algorithms.
The present invention will be more fully understood upon consideration of the detailed description below, taken together with the accompanying drawings.